Operation Support System Research Articles

Development of a Data-driven Decision Support System for the Efficient Operation of the Research Reactor Secondary System

The High-flux Advanced Neutron Application Reactor (HANARO) is a research reactor in the Republic of Korea. The secondary cooling system of HANARO releases heat into the atmosphere via cooling towers and fans. Because a part of the system is exposed to the external atmosphere, the behavior of the secondary cooling system is coupled with atmospheric conditions. However, no procedure exists to reflect such coupling, and the cooling fans are empirically controlled by the operators. This empirical decision-making may increase the workload and reduce operational efficiency; therefore, the cooling fan operational schemes must be improved. In this study, a decision support system is developed based on two artificial intelligence (AI) models to aid operators’ decision-making in controlling the cooling fans. During model development, data augmentation was applied to alleviate data imbalances. Additionally, a graphical user interface-based decision support system prototype was developed based on the models to provide operators with more comprehensive summaries of the information related to cooling-fan operations and outputs from the AI models. The models demonstrated acceptable performance on the testing data. The prototype system was installed in the main control room of HANARO and evaluated for its usability improvements and feasibility in practical applications.

The effect of failure on energy efficiency in maritime vessels autopilot systems

The increasing demands for energy efficiency and environmental sustainability in the maritime industry have underscored the critical importance of optimizing autopilot systems, which, despite their significance, are often overlooked in ship energy efficiency management. The objective of this study is to enhance the energy efficiency of ships by focusing on the efficiency of autopilot systems, which play a significant role in the management of energy efficiency. The research emphasizes the need for effective decision support systems for ship operators, not only for optimizing ship speeds but also for making informed operational decisions. By utilizing Fuzzy Fault Tree Analysis (FFTA), the study identifies and prioritizes the causes of efficiency losses in autopilot systems and examines their frequency. Based on expert opinions, the research delves into the complexity of autopilot systems and the interactions among various components. Notably, the study highlights the impact of multiple factors on the efficiency of complex autopilot systems, elucidating their relationships through Minimum Cut Sets (MCS) analysis. Furthermore, attention was drawn to the “Improper Alarm Input” event caused by insufficient knowledge and awareness among ship operators, which hinders the effective use of autopilot systems. The findings demonstrate that decision support systems can increase energy efficiency and contribute to the reduction of operational errors by reducing the human factor, which is 99% effective on the “Inefficient Heading Control System”. Additionally, proper utilization of autopilot systems can lead to a decrease in a ship's carbon footprint and operating costs. Overall, the results can affect strategic decisions in ship energy efficiency management and encourage significant steps toward achieving International Maritime Organization's (IMO) sustainability objectives.

Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.

직업계고 학점제 공동교육과정 운영 실태 분석을 통한 지원 방안 탐색

The purpose of this study is to explore support measures based on the current status and needs of the joint curriculum of the vocational high school credit system in order to overcome the limitations of the curriculum operation of uniform and rigid schools. The results of the survey for 142 vocational high school teachers who operated the “joint Curriculum” in 2023 were used to analyze the actual condition of the “joint Curriculum Operation” of vocational high schools. As a result of the study, it suggested that the government prepare guidelines for the operation of the joint curriculum for vocational high schools, prepare a support system for the smooth operation of the joint curriculum, find diversity in the operation model of the joint curriculum considering the characteristics of the school, prepare guidelines for teaching and evaluation of the joint curriculum for vocational high schools, share best practices, seek support measures for each role of teachers participating in the joint curriculum, strengthen the role of provincial and provincial offices of education to revitalize the joint curriculum for vocational high schools, and improve the administrative work of the joint curriculum.

A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response

Secure operation of a DC microgrid with high penetration of renewables and electric vehicle load is challenging. This paper proposes a decentralized energy management scheme for a grid-connected renewable integrated community DC microgrid considering the water-energy nexus. Uncertainties of renewable generation, plug-in electric vehicle load, electricity demand, electricity price in the day-ahead wholesale market, ambient temperature, and water demand are modelled using a probabilistic approach. Correlation between the input random variables is modelled using Copula theory. Flexibilities on the consumer side across multiple entities (temperature-dependent loads, electric vehicles, and water supply system storage) are coordinated to form an “integrated demand response entity”, which is further coordinated with the DC microgrid operator side flexibility (electrochemical storage) to support system operation. A distributed dynamic pricing scheme is used to implement the integrated demand response program. The objectives of the energy management scheme are to minimize the DC microgrid operator’s operating cost and consumers’ electricity cost. The decentralized algorithm is solved by the “alternating direction method of multipliers”. Simulation studies on a six-bus DC microgrid test system demonstrate that the proposed strategy reduces the operating cost of the DC microgrid operator by ∼19.28% and the electricity cost of the consumers by ∼13.82%.

Joint modeling and dynamic analysis of the microenvironment and life support performance of extravehicular spacesuits

The ability to maintain the microenvironment and life-support systems of an extravehicular spacesuit is an important factor in determining the duration of extravehicular activity (EVA). This paper introduces a joint human-spacesuit microenvironment dynamic model. The paper presents novel simplified human body models and analyzes expendable substances. These models can reasonably predict spacesuit safety performance, correctly respond to changes in loads, and aid in the optimization of the intensity of EVAs. According to the simulations, an 8-h EVA consumes approximately 1 kg of LiOH and 2.7 kg of water under the designed working conditions. Liquid cooling systems are the primary thermal management devices in microenvironments. Activity intensity and liquid cooling system flow rate are two important factors that influence the spacesuit microenvironment and life support material consumption. Activity intensity has a significant impact on LiOH consumption, with a threefold increase in metabolic heat increases LiOH consumption by about 2.5 times. Activity intensity plays an important role in the life-support performance of a spacesuit, and proper scheduling is critical to the efficiency and safety of EVAs. The material consumption model can estimate material consumption during the mission scheduling phase, resulting in efficient and dependable operation of the life support system.

Solid waste management and resource recovery during the 4-crew 180-day CELSS integrated experiment

In order to explore the management and treatment methods of solid waste in the Controlled Ecological Life Support System (CELSS) of future lunar bases, during the 4-crew 180-day integrated experiment, the Solid Waste Management and Treatment System (SWMTS) was built, in which the treatment of recyclable solid waste such as inedible plant parts and human excrement was completed through a combination of biological aerobic composting and high-temperature oxidation. Basic data on the types and amounts of solid waste generated during the 4-crew 180-day experiment mission were obtained. There were six types of solid wastes, including the work support wastes, the household support wastes, the plant cultivation wastes, the plant-based wastes, and crew feces. The daily average production was 0.67, 1.4, 0.32, 8.48, 0.534 kg/d, respectively. The proportion of plant-based wastes was high as 74.3 %, indicating that it was the most important part. By closed-loop air drying and graded crushing, all 1526.97 kg of plant-based waste was treated, with water recovery (about 1163.87 kg), as well as volume reduction and stabilization treatment. By incineration and aerobic composting treatment, 67.3 % (244.4 kg) of the plant-based wastes (dry weight) and all of the feces (96.26 kg) were converted, providing 339.54 kg carbon dioxide for plant growth. And 90.6 kg organic fertilizer was obtained. The fertilizer was highly mature, met safety requirements, and effectively improved lettuce yield. The recycling rate of renewable solid waste during the experiment reached 89.8 %. The efficient circulation of solid waste had been achieved during the 4-crew 180-day integrated experiment. The long-time experimental results have shown that the established solid wastes management and treatment system can timely treat biomass solid waste such as inedible parts of plants and crew feces, achieve timely recovery of water in such solid waste, and recycle carbon and other elements, which effectively improved the material closure of the system and ensured the successful 4-crew 180-day experiment. This work also maybe lay the foundation for the construction and operation of an ecological life support system for future lunar bases.

Using smart card data to develop origin-destination matrix-based business analytics for bus rapid transit systems: case study of Jakarta, Indonesia

Bus rapid transit systems (BRT) have been an indispensable public transportation pillar, especially in densely populated regions. Accurate insight into the BRT network's utilization is vital in BRT resource allocation planning contexts. This research focuses on how operators can utilize passengers’ smart card data to develop origin-destination (OD) matrix-based business analytics. This research proposes a hybrid approach combining trip chaining, direct pairing, mode estimation methods, and visual analytics development. The novel approach is robust in handling incomplete smart card data transactions to generate origin-destination matrices and corresponding visual analytics as decision support systems for the BRT operators. As a case study, we applied and validated the proposed analytics to more than 20.6 million smart card transactions in one of the largest global BRT systems in Jakarta, Indonesia.

Agent-based decision-support model for bus route redesign in networks of small cities and towns: case study of Agder, Norway

Small cities and towns often struggle to provide high-quality public transport services to daily commuters. This is reflected in the modal split, where the share of car users dominates. Such a problem requires a modern solution, where transport planners can verify the impact of potential transport network improvements on the travel behavior of the residents before the changes are actually deployed. This study aims to demonstrate the usefulness of employing an agent-based simulation tool in the decision process for redesigning an express service regional bus route connecting a network of small cities and towns. The model was initially developed as a Mobility as a Service simulation solution for suburban areas of European metropolises. The model is adapted and applied to a case study for the region of Agder, Norway, simulating the impact of nine different scenarios on the patronage of a specific bus route. The simulation model proposes to upgrade the classic agent structure to a persona profile designed specifically for the case study. The main objective of this research is to identify the scenario that maximizes patronage while minimizing total route travel time and additional costs. The results suggest that the proposed model can be successfully adapted from suburban metropolitan areas to the realities of the considered case study, and potentially other similar regions. Specifically, out of the nine proposed scenarios, the model identified four promising ones. One of the four scenarios also fits the cost constraints imposed by the transport provider. The model provides a solid approach for analyzing complex transport systems that are practically impossible to consider in detail if the analysis is done without computer support. Thus, the results can be used as a decision support system for public transport planning and operations in networks of small cities and towns.

MariData – Digital Twin for Optimal Vessel Operations Impacting Ship Design

Energy efficiency is a key element for reduced shipping emission to meet future environmental regulations. Both design and operation play an important role to meet this goal and the perfection of the interplay between these aspects promises quick improvements to meet the requirements of short term emission standards. The MariData project [https://maridata.org] developed a forward-looking energy management and decision support system (DSS) for ship operation based on rational methods and data created during ship design and sets out to bridge the gap between design and optimized operation. The “digital performance twin” of a vessel, which is based on design data, is enhanced with lifecycle data covering the entire operational envelope and provides valuable feedback into design processes.

The Planetary Life Support Mechanism on the Earth: Problems and Prospects

Relevance. The study's relevance is conditioned by identifying a previously unknown planetary life support mechanism, including the creation and preservation of conditions and life itself on Earth. Purpose. The purpose of the study was to reveal the planetary life support mechanism and to understand the regularities of the origin and continuous action of terrestrial processes, which are the conditions necessary for life on Earth. Methodology. The main research method is experimental modelling of electromagnetic interactions in the Sun-atmosphere-Earth system. Results. It has been established that part of the solar energy that comes to the Earth without penetrating its surface ionises the atmosphere, sequentially transforming, creates and preserves the terrestrial processes necessary for life and turns into geoelectricity that creates the Earth�s electromagnetic field � a constantly operating source of energy in the Earth�s crust. The solar energy penetrating to the surface of the Earth, which has a periodic effect on it, is supposed to preserve the thermal regime of the planet, together with the additional heat of the induction current and the rotation of the Earth and the atmosphere, created by the flow of solar energy that does not penetrate to its surface. Conclusions. It is shown that all the electromagnetic energy of solar radiation continuously flowing to the Earth is intended and used by the planetary life support mechanism. The findings have practical implications for monitoring the operation of the planetary life support system, understanding how solar energy sustains living organisms, studying the evolution and variability of weather patterns, and exploring the potential for managing these processes. Keywords: rotation of the Earth; solar energy; thermal regime; electromagnetic field; induction current; terrestrial processes; geoelectricity; meteorological conditions

Development of an AI-based remaining trip time prediction system for nuclear power plants

In abnormal states of nuclear power plants (NPPs), operators undertake mitigation actions to restore a normal state and prevent reactor trips. However, in abnormal states, the NPP condition fluctuates rapidly, which can lead to human error. If human error occurs, the condition of an NPP can deteriorate, leading to reactor trips. Sudden shutdowns, such as reactor trips, can result in the failure of numerous NPP facilities and economic losses. This study develops a remaining trip time (RTT) prediction system as part of an operator support system to reduce possible human errors and improve the safety of NPPs. The RTT prediction system consists of an algorithm that utilizes artificial intelligence (AI) and explainable AI (XAI) methods, such as autoencoders, light gradient-boosting machines, and Shapley additive explanations. AI methods provide diagnostic information about the abnormal states that occur and predict the remaining time until a reactor trip occurs. The XAI method improves the reliability of AI by providing a rationale for RTT prediction results and information on the main variables of the status of NPPs. The RTT prediction system includes an interface that can effectively provide the results of the system.

Smart Billing Software

The set of solutions offered by the Business Operation Support System revolves around Smart Billing Software. Because billing is the nerve centre of the company, it oversees all the important data, including product catalogues, customers, rates, taxes, financial accounting, invoices, payments, debit and credit notes, dual ledgers, support for multiple currencies, and more. You can manage any business operation involving metered charges, recurring charges, event-based charges, and ad-hoc charges with the help of Smart Billing Software's engine, which offers unparalleled flexibility in setting up any kind of product, product packages, user-defined rating structures, tax rules, and more. With Smart Billing Software, any service model and offering can be easily integrated into a single platform. It is compatible with both pre-paid and post-paid payment systems. Due to its open API foundation, Smart Billing Software is highly compatible with other BOSS products, allowing for the construction of comprehensive solutions. Smart Billing is a pre-integrated component of Xsinfosol Solutions and is one of the BOSS solutions offered by Xsinfosol.

Quality control in manufacturing – review and challenges on robotic applications

ABSTRACT Quality control methods and techniques have been investigated in different fields of manufacturing during the last decades. The introduction of robots in manufacturing processes has created a rapid deployment of robotic applications, leading to an increased research interest in the aspect of quality control in such industrial environments. This paper summarizes and presents a review of recent research and progress on quality control technologies, focusing on robot-based production systems. The role of different parameters affecting the control of quality in robotic tasks is also discussed, incorporating the impact of operator support systems in human robot collaborative environments. Research gaps and implications of quality control in robotic applications are also described, and a future outlook of research in the particular field is provided.

Proposal of a novel AI-based plant operator support system for the safety of nuclear power plants

Proposal of a novel AI-based plant operator support system for the safety of nuclear power plants

Trackside sensors in unattended train mainline systems - A case study of alarm logs from Sweden

Implementing unattended train operation on the mainline could make the railway more competitive by reducing operating costs since there will be no staff onboard the train. This will, however, lead to new challenges. One of those challenges is how to deal with manually controlling trackside sensor alarms. In this paper, we study all trackside sensor alarms (hotbox/hotwheel and wheel damage) in Sweden for one year (2019) to study their frequency and context. The results show that freight trains have 10 times higher frequency for alarms per train kilometer than passenger trains. There are statistically significant seasonal and climate zone differences. The highest frequency of trackside alarms occurs in wintertime in the colder climate zone. The results can be used in the development of unattended train operation support systems on the mainline.

Research on Edge-Computing-Based High Concurrency and Availability “Cloud, Edge, and End Collaboration” Substation Operation Support System and Applications

With the continuous promotion of digital transformation in the field of power transformation, the diversification of application scenarios, and the scale of pilot construction, the real-time, concurrency, and security requirements for data fusion and application support of the power monitoring system, management information system, and other business platforms are getting higher and higher, and this paper puts forward a high concurrency and availability “cloud-side-end collaboration” based on edge computing. This paper proposes a high concurrency and availability “cloud, edge and end collaboration” architecture based on edge computing for substation operation support systems. First, this paper summarizes the development status of domestic substation operation support systems and analyzes the advantages and disadvantages of various technical architectures. Then, a “cloud-side-end cooperative” substation operation support system architecture with “high real-time, high concurrency, high security and high stability” is proposed, which focuses on remote inspection, remote operation, and remote safety control of substation businesses from the perspective of engineering applications. It realizes transparent monitoring of equipment operation, unified management of operation data, and integration of production command and decision-making; solves the problems of dispersed coexistence of multiple systems for dispatching, monitoring, analysis, management, and other businesses, switching between multiple systems, and insufficient real-time and stability of the system; and controls the risks of the grid, reduces the potential safety hazards, and solves the contradiction between the continuous growth of the grid equipment and the shortage of production personnel. The results of engineering application examples show that the proposed architecture compared with the existing system architecture has greater advantages and can meet the requirements of large-scale access to the substation, with feasible popularization and application.

The Development of High Availability Database Infrastructure for OSS Projects with Monitoring Systems in Cloud Computing Environments

This study addressed a critical problem in the Kominfo's Operation Support System (OSS) project, which significantly impacted business operations. The outage of the MongoDB database, a vital component of OSS, resulted in server crashes and data damage. To overcome this issue, the OSS team initiated a project to migrate to PostgreSQL for high availability and improved database performance. The implementation involved the use of HA PostgreSQL technology, with multiple connected servers sharing data in real-time. Through functional and performance testing, the study has demonstrated that the HA PostgreSQL system increased database availability, managed server failures, and facilitated effective cluster administration. The findings of this research can guide the development of the OSS project's IT infrastructure and serve as a reference for similar projects utilizing HA PostgreSQL technology.

Synthetic data formation for machine learning recognition of underwater objects

The main problem of machine learning for control systems of unmanned underwater vehicles is objectively very small samples of real data. The study aim was to develop an approach to the creation of synthetic data describing underwater objects for as the samples for training and validation in machine learning of control systems for auton-omous unmanned underwater vehicles. The subject of the study was a variety of underwater objects because real information about the shape, size and their external images is very limited. The data augmentation method was used, which makes it possible to obtain additional data on underdetermined objects of observation from the initial data while maintaining the classification features. Eight models have been developed that imitate the influence of various factors of the aquatic environment and allow using various augmentation methods (changing the position, adding noise, glare to the image, defocusing to create fuzziness; fragmentation, etc.) to obtain an almost unlimited number of images of any objects of man-made activity immersed in underwater environment, to varying degrees similar to the reference. Examples of the use of augmentation models that take into account changes in illumina-tion, transparency and the presence of an underwater landscape are given. Such synthetic (model) images may be the basis of a training set for machine learning to recognize and identify underwater objects. The trained model can be used as the basis of a decision support system for operators of remote-controlled unmanned underwater vehicles and as the basis for building control systems for autonomous uninhabited underwater vehicles for moni-toring underwater spaces.

DEVELOPMENT OF A DSS MODULE WHEN PREPARING ASSESSMENTS FOR IT COMPANY CLIENTS

The article considers the relevance of the search for toolkit for objective software development complexity and timing assessment. The key purpose of this toolkit is to provide an IT company with the opportunity to objectively assess the labor intensity, the cost of a future software product at an early stage, on the one hand, and to ensure transparency in justifying an acceptable transaction price for the customer, on the other hand. A special feature of the IT industry is the intangible nature of the created product and, as a result, the challenge of choosing methods (metrics) for labor intensity assessment and predicting the timing of the software product implementation, which allows obtaining realistic data without serious time and financial costs, poses a certain difficulty. The results of the analysis and comparison of the most common methods of software development complexity assessment (in particular, IFPUG, UCP, COCOMOII, etc.), including identification of advantages and limitations, are presented. A generalized algorithm for the decision support system operation is proposed to reduce time and increase the validity of calculations when drawing up estimates by an IT company analyst as a part of the preparation of technical specifications. A description of individual screen forms of the decision support system designed module is provided.